Machine-Learning-Based Additive Manufacturing Using Manufacturing Data

1. A machine-learning-based additive manufacturing method comprising: processing, with a machine-learning model, an input vector describing a new part transaction to provide at least one part optimization output and at least one command initiation output to configure additive manufacturing of a new part, the at least one part optimization output comprising a set of support curves describing support structures to be additively manufactured along with the new part, the support structures being sacrificial structures that support the additive manufacturing of the new part, and that are not components of the new part but that are configured to be removed after additive-manufacturing, the set of support curves reducing the amount of time needed to additively manufacture the part or reducing the amount of time needed to remove the support structures after additive manufacturing, as compared to a different set of support curves, the at least one part optimization output further comprising a fabrication spatial orientation of the new part that defines the spatial orientation at which the new part is to be additively manufactured and that reduces the amount of time needed to additively manufacture the part or the amount of material used in the support structures as compared to a different spatial orientation, the at least one part optimization output further comprising a fabrication slicing resolution of the new part that defines a resolution at which the new part is to be additively manufactured and that reduces the amount of time needed to additively manufacture the part as compared to a different fabrication slicing resolution; and fabricating the new part using additive manufacturing based on the at least one part optimization output and the at least one command initiation output, wherein the machine-learning model is trained based on entries in a user experience database, entries in the user experience database each including at least data defining requirements for an additively manufactured part previously fabricated or attempted to be fabricated, specifications describing an additive manufacturing fabrication device, a selection of a raw material type fed to the fabrication device for fabrication of the additively manufactured part, a fabrication spatial orientation of the additively manufactured part within the fabrication device, a fabrication slicing resolution of the additively manufactured part, and a toolpath taken by the fabrication device in fabricating the additively manufactured part, wherein the machine-learning model provides the at least one part optimization output including the set of support curves describing the support structures, the fabrication spatial orientation of the new part, and the fabrication slicing resolution of the new part based on the input vector describing the new part transaction and on the entries in the user experience database.

2. The method of claim 1 , wherein each entry in the user experience database is a timestamped block in a blockchain.

3. The method of claim 1 , wherein the at least one part optimization output comprises a selection of material type from which the new part is additively manufactured.

4. The method of claim 1 , wherein the at least one command initiation output comprises a command sent to a 3D printer to initiate printing along a toolpath generated by the machine-learning model based on the input vector.

5. A system for machine-learning-based additive manufacturing, the system comprising: a user experience database stored on or distributed across one or more non-transitory computer-readable memories, entries in the user experience database each including at least data defining requirements for an additively manufactured part previously fabricated or attempted to be fabricated, specifications describing an additive manufacturing fabrication device, a selection of a raw material type fed to the fabrication device for fabrication of the additively manufactured part, a fabrication spatial orientation of the additively manufactured part within the fabrication device, a fabrication slicing resolution of the additively manufactured part, and a toolpath taken by the fabrication device in fabricating the additively manufactured part; a processor configured to execute instructions implementing a machine-learning model trained based on the entries in the user experience database, the machine-learning model being configured to process an input vector describing a new part transaction to provide at least one part optimization output and at least one command initiation output to configure additive manufacturing of a new part, the at least one part optimization output comprising a set of support curves describing support structures to be additively manufactured along with the new part, the support structures being sacrificial structures that support the additive manufacturing of the new part, and that are not components of the new part but that are configured to be removed after additive-manufacturing, the set of support curves reducing the amount of time needed to additively manufacture the part or reducing the amount of time needed to remove the support structures after additive manufacturing, as compared to a different set of support curves, the at least one part optimization output further comprising a fabrication spatial orientation of the new part that defines the spatial orientation at which the new part is to be additively manufactured and that reduces the amount of time needed to additively manufacture the part or the amount of material used in the support structures as compared to a different spatial orientation, the at least one part optimization output further comprising a fabrication slicing resolution of the new part that defines a resolution at which the new part is to be additively manufactured and that reduces the amount of time needed to additively manufacture the part as compared to a different fabrication slicing resolution, wherein the machine-learning model provides the at least one part optimization output including the set of support curves describing the support structures, the fabrication spatial orientation of the new part, and the fabrication slicing resolution of the new part based on the input vector describing the new part transaction and on the entries in the user experience database.

6. The system of claim 5 , wherein each entry in the user experience database is a timestamped block in a blockchain.

7. The system of claim 5 , wherein the at least one part optimization output comprises a selection of material type from which the new part is additively manufactured.

8. The system of claim 5 , wherein the at least one command initiation output comprises a command sent to a 3D printer to initiate printing along a toolpath generated by the machine-learning model based on the input vector.

9. One or more non-transitory computer-readable media storing instructions that when executed by a computer processor, cause the processor to: process, with a machine-learning model trained on a user experience database comprising a plurality of entries, an input vector describing a new part transaction to provide at least one part optimization output and at least one command initiation output to configure additive manufacturing of a new part, wherein entries in the user experience database each include at least data defining requirements for an additively manufactured part previously fabricated or attempted to be fabricated, specifications describing an additive manufacturing fabrication device, a selection of a raw material type fed to the fabrication device for fabrication of the additively manufactured part, a fabrication spatial orientation of the additively manufactured part within the fabrication device, a fabrication slicing resolution of the additively manufactured part, and a toolpath taken by the fabrication device in fabricating the additively manufactured part, wherein the at least one part optimization output comprises a set of support curves describing support structures to be additively manufactured along with the new part, the support structures being sacrificial structures that support the additive manufacturing of the new part, and that are not components of the new part but that are configured to be removed after additive-manufacturing, the set of support curves reducing the amount of time needed to additively manufacture the part or reducing the amount of time needed to remove the support structures after additive manufacturing, as compared to a different set of support curves, wherein the at least one part optimization output further comprises a fabrication spatial orientation of the new part that defines the spatial orientation at which the new part is to be additively manufactured and that reduces the amount of time needed to additively manufacture the part or the amount of material used in the support structures as compared to a different spatial orientation, wherein the at least one part optimization output further comprises a fabrication slicing resolution of the new part that defines a resolution at which the new part is to be additively manufactured and that reduces the amount of time needed to additively manufacture the part as compared to a different fabrication slicing resolution, and wherein the machine-learning model provides the at least one part optimization output including the set of support curves describing the support structures, the fabrication spatial orientation of the new part, and the fabrication slicing resolution of the new part based on the input vector describing the new part transaction and on the entries in the user experience database.

10. The media of claim 9 , wherein each entry in the user experience database is a timestamped block in a blockchain.

11. The media of claim 9 , wherein the at least one part optimization output comprises a selection of material type from which the new part is additively manufactured.